bert-base-NER-finetuned-ner
This model is a fine-tuned version of dslim/bert-base-NER on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9704
- 0 Precision: 0.9706
- 0 Recall: 0.9413
- 0 F1-score: 0.9558
- 1 Precision: 0.8027
- 1 Recall: 0.9205
- 1 F1-score: 0.8575
- 2 Precision: 0.7853
- 2 Recall: 0.8165
- 2 F1-score: 0.8006
- 3 Precision: 0.7817
- 3 Recall: 0.8603
- 3 F1-score: 0.8191
- Accuracy: 0.9272
- Macro avg Precision: 0.8351
- Macro avg Recall: 0.8847
- Macro avg F1-score: 0.8583
- Weighted avg Precision: 0.9313
- Weighted avg Recall: 0.9272
- Weighted avg F1-score: 0.9285
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
Training results
Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 1 Precision | 1 Recall | 1 F1-score | 2 Precision | 2 Recall | 2 F1-score | 3 Precision | 3 Recall | 3 F1-score | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 67 | 0.3241 | 0.9901 | 0.8116 | 0.8920 | 0.5586 | 0.9694 | 0.7088 | 0.4424 | 0.8807 | 0.5890 | 0.6615 | 0.8696 | 0.7514 | 0.8343 | 0.6631 | 0.8829 | 0.7353 | 0.8979 | 0.8343 | 0.8495 |
No log | 2.0 | 134 | 0.3219 | 0.9882 | 0.8544 | 0.9164 | 0.6282 | 0.9480 | 0.7556 | 0.5375 | 0.8318 | 0.6531 | 0.6460 | 0.9106 | 0.7558 | 0.8665 | 0.7000 | 0.8862 | 0.7702 | 0.9064 | 0.8665 | 0.8763 |
No log | 3.0 | 201 | 0.3126 | 0.9927 | 0.8353 | 0.9072 | 0.5873 | 0.9725 | 0.7323 | 0.5213 | 0.8624 | 0.6498 | 0.6578 | 0.9199 | 0.7671 | 0.8561 | 0.6898 | 0.8975 | 0.7641 | 0.9062 | 0.8561 | 0.8677 |
No log | 4.0 | 268 | 0.3805 | 0.9851 | 0.8936 | 0.9371 | 0.7105 | 0.9419 | 0.8100 | 0.6166 | 0.8410 | 0.7115 | 0.7001 | 0.9218 | 0.7958 | 0.8979 | 0.7531 | 0.8996 | 0.8136 | 0.9196 | 0.8979 | 0.9035 |
No log | 5.0 | 335 | 0.4058 | 0.9839 | 0.9028 | 0.9416 | 0.6786 | 0.9587 | 0.7947 | 0.6887 | 0.8593 | 0.7646 | 0.7718 | 0.9069 | 0.8339 | 0.9064 | 0.7807 | 0.9069 | 0.8337 | 0.9246 | 0.9064 | 0.9110 |
No log | 6.0 | 402 | 0.4349 | 0.9833 | 0.9130 | 0.9468 | 0.7246 | 0.9373 | 0.8173 | 0.6786 | 0.8716 | 0.7631 | 0.7649 | 0.9088 | 0.8306 | 0.9130 | 0.7878 | 0.9077 | 0.8395 | 0.9275 | 0.9130 | 0.9169 |
No log | 7.0 | 469 | 0.4379 | 0.9839 | 0.9184 | 0.9500 | 0.7308 | 0.9465 | 0.8248 | 0.7072 | 0.8716 | 0.7808 | 0.7755 | 0.9069 | 0.8361 | 0.9179 | 0.7994 | 0.9108 | 0.8479 | 0.9308 | 0.9179 | 0.9214 |
0.2085 | 8.0 | 536 | 0.4750 | 0.9862 | 0.8964 | 0.9391 | 0.6702 | 0.9694 | 0.7925 | 0.7038 | 0.8502 | 0.7701 | 0.7369 | 0.9181 | 0.8176 | 0.9028 | 0.7743 | 0.9085 | 0.8298 | 0.9236 | 0.9028 | 0.9079 |
0.2085 | 9.0 | 603 | 0.5353 | 0.9817 | 0.9225 | 0.9512 | 0.7443 | 0.9526 | 0.8357 | 0.7342 | 0.8532 | 0.7893 | 0.7751 | 0.9050 | 0.8351 | 0.9207 | 0.8088 | 0.9083 | 0.8528 | 0.9315 | 0.9207 | 0.9236 |
0.2085 | 10.0 | 670 | 0.5730 | 0.9786 | 0.9325 | 0.9550 | 0.7920 | 0.9434 | 0.8611 | 0.7413 | 0.8502 | 0.7920 | 0.7722 | 0.8901 | 0.8270 | 0.9263 | 0.8211 | 0.9040 | 0.8588 | 0.9338 | 0.9263 | 0.9285 |
0.2085 | 11.0 | 737 | 0.5801 | 0.9787 | 0.9199 | 0.9484 | 0.7404 | 0.9419 | 0.8291 | 0.7199 | 0.8410 | 0.7757 | 0.7679 | 0.8994 | 0.8285 | 0.9166 | 0.8017 | 0.9005 | 0.8454 | 0.9276 | 0.9166 | 0.9197 |
0.2085 | 12.0 | 804 | 0.7227 | 0.9675 | 0.9526 | 0.96 | 0.8496 | 0.8807 | 0.8649 | 0.8170 | 0.7920 | 0.8043 | 0.7921 | 0.8939 | 0.8399 | 0.9337 | 0.8565 | 0.8798 | 0.8673 | 0.9356 | 0.9337 | 0.9343 |
0.2085 | 13.0 | 871 | 0.6296 | 0.9744 | 0.9421 | 0.9579 | 0.8331 | 0.9159 | 0.8725 | 0.7568 | 0.8471 | 0.7994 | 0.7794 | 0.8883 | 0.8303 | 0.9309 | 0.8359 | 0.8983 | 0.8650 | 0.9356 | 0.9309 | 0.9325 |
0.2085 | 14.0 | 938 | 0.7074 | 0.9728 | 0.9397 | 0.9559 | 0.8070 | 0.9205 | 0.8600 | 0.7690 | 0.8349 | 0.8006 | 0.7804 | 0.8734 | 0.8243 | 0.9278 | 0.8323 | 0.8921 | 0.8602 | 0.9326 | 0.9278 | 0.9293 |
0.0385 | 15.0 | 1005 | 0.7392 | 0.9714 | 0.9441 | 0.9576 | 0.8425 | 0.8914 | 0.8663 | 0.7634 | 0.8287 | 0.7947 | 0.7721 | 0.8957 | 0.8293 | 0.9299 | 0.8373 | 0.8900 | 0.8620 | 0.9340 | 0.9299 | 0.9313 |
0.0385 | 16.0 | 1072 | 0.7589 | 0.9741 | 0.9399 | 0.9567 | 0.8003 | 0.9190 | 0.8555 | 0.7604 | 0.8349 | 0.7959 | 0.7876 | 0.8771 | 0.8300 | 0.9281 | 0.8306 | 0.8927 | 0.8595 | 0.9331 | 0.9281 | 0.9297 |
0.0385 | 17.0 | 1139 | 0.7045 | 0.9724 | 0.9380 | 0.9549 | 0.7847 | 0.9251 | 0.8491 | 0.7624 | 0.8440 | 0.8012 | 0.8056 | 0.8641 | 0.8338 | 0.9266 | 0.8313 | 0.8928 | 0.8597 | 0.9318 | 0.9266 | 0.9282 |
0.0385 | 18.0 | 1206 | 0.7735 | 0.9698 | 0.9437 | 0.9566 | 0.8043 | 0.9174 | 0.8571 | 0.7721 | 0.8287 | 0.7994 | 0.8074 | 0.8510 | 0.8286 | 0.9286 | 0.8384 | 0.8852 | 0.8604 | 0.9322 | 0.9286 | 0.9298 |
0.0385 | 19.0 | 1273 | 0.7184 | 0.9735 | 0.9399 | 0.9564 | 0.8150 | 0.9159 | 0.8625 | 0.7439 | 0.8440 | 0.7908 | 0.7863 | 0.8771 | 0.8292 | 0.9282 | 0.8297 | 0.8942 | 0.8597 | 0.9332 | 0.9282 | 0.9298 |
0.0385 | 20.0 | 1340 | 0.7814 | 0.9741 | 0.9341 | 0.9537 | 0.7875 | 0.9235 | 0.8501 | 0.7535 | 0.8226 | 0.7865 | 0.7581 | 0.8696 | 0.8101 | 0.9229 | 0.8183 | 0.8875 | 0.8501 | 0.9293 | 0.9229 | 0.9249 |
0.0385 | 21.0 | 1407 | 0.8279 | 0.9696 | 0.9445 | 0.9569 | 0.8201 | 0.9128 | 0.8640 | 0.7768 | 0.8196 | 0.7976 | 0.7880 | 0.8585 | 0.8217 | 0.9289 | 0.8386 | 0.8838 | 0.8601 | 0.9323 | 0.9289 | 0.9301 |
0.0385 | 22.0 | 1474 | 0.7268 | 0.9724 | 0.9332 | 0.9524 | 0.7704 | 0.9388 | 0.8463 | 0.7647 | 0.8349 | 0.7982 | 0.7818 | 0.8473 | 0.8132 | 0.9224 | 0.8223 | 0.8885 | 0.8525 | 0.9287 | 0.9224 | 0.9243 |
0.0127 | 23.0 | 1541 | 0.8197 | 0.9698 | 0.9445 | 0.9570 | 0.8078 | 0.9190 | 0.8598 | 0.7928 | 0.8073 | 0.8 | 0.7973 | 0.8641 | 0.8293 | 0.9294 | 0.8419 | 0.8837 | 0.8615 | 0.9327 | 0.9294 | 0.9305 |
0.0127 | 24.0 | 1608 | 0.8221 | 0.9722 | 0.9447 | 0.9582 | 0.8197 | 0.9037 | 0.8596 | 0.7718 | 0.8379 | 0.8035 | 0.7933 | 0.8790 | 0.8339 | 0.9307 | 0.8392 | 0.8913 | 0.8638 | 0.9344 | 0.9307 | 0.9320 |
0.0127 | 25.0 | 1675 | 0.8098 | 0.9735 | 0.9373 | 0.9550 | 0.7766 | 0.9358 | 0.8488 | 0.7928 | 0.8073 | 0.8 | 0.7809 | 0.8696 | 0.8229 | 0.9257 | 0.8310 | 0.8875 | 0.8567 | 0.9314 | 0.9257 | 0.9274 |
0.0127 | 26.0 | 1742 | 0.8023 | 0.9710 | 0.9404 | 0.9554 | 0.7897 | 0.9358 | 0.8565 | 0.7813 | 0.8196 | 0.8 | 0.8035 | 0.8529 | 0.8275 | 0.9275 | 0.8364 | 0.8872 | 0.8599 | 0.9319 | 0.9275 | 0.9288 |
0.0127 | 27.0 | 1809 | 0.7750 | 0.9748 | 0.9373 | 0.9557 | 0.7897 | 0.9358 | 0.8565 | 0.7591 | 0.8287 | 0.7924 | 0.7963 | 0.8808 | 0.8364 | 0.9276 | 0.8300 | 0.8957 | 0.8603 | 0.9333 | 0.9276 | 0.9293 |
0.0127 | 28.0 | 1876 | 0.9205 | 0.9673 | 0.9465 | 0.9568 | 0.8220 | 0.9037 | 0.8609 | 0.7861 | 0.7982 | 0.7921 | 0.7925 | 0.8603 | 0.8250 | 0.9288 | 0.8420 | 0.8772 | 0.8587 | 0.9314 | 0.9288 | 0.9297 |
0.0127 | 29.0 | 1943 | 0.7887 | 0.9726 | 0.9376 | 0.9548 | 0.7695 | 0.9343 | 0.8439 | 0.7756 | 0.8349 | 0.8041 | 0.8057 | 0.8492 | 0.8268 | 0.9256 | 0.8308 | 0.8890 | 0.8574 | 0.9311 | 0.9256 | 0.9273 |
0.0052 | 30.0 | 2010 | 0.8106 | 0.9778 | 0.9371 | 0.9570 | 0.7861 | 0.9327 | 0.8531 | 0.7658 | 0.8502 | 0.8058 | 0.7897 | 0.8883 | 0.8361 | 0.9288 | 0.8299 | 0.9021 | 0.8630 | 0.9351 | 0.9288 | 0.9307 |
0.0052 | 31.0 | 2077 | 0.8659 | 0.9699 | 0.9421 | 0.9558 | 0.8022 | 0.9113 | 0.8533 | 0.7929 | 0.8196 | 0.8060 | 0.7922 | 0.8734 | 0.8308 | 0.9281 | 0.8393 | 0.8866 | 0.8615 | 0.9319 | 0.9281 | 0.9293 |
0.0052 | 32.0 | 2144 | 0.8154 | 0.9722 | 0.9389 | 0.9553 | 0.7878 | 0.9251 | 0.8509 | 0.7768 | 0.8410 | 0.8076 | 0.7986 | 0.8641 | 0.8301 | 0.9272 | 0.8339 | 0.8923 | 0.8610 | 0.9321 | 0.9272 | 0.9287 |
0.0052 | 33.0 | 2211 | 0.8569 | 0.9727 | 0.9432 | 0.9577 | 0.8086 | 0.9174 | 0.8596 | 0.7878 | 0.8287 | 0.8077 | 0.7953 | 0.8827 | 0.8367 | 0.9307 | 0.8411 | 0.8930 | 0.8654 | 0.9347 | 0.9307 | 0.9320 |
0.0052 | 34.0 | 2278 | 0.8868 | 0.9705 | 0.9432 | 0.9566 | 0.8011 | 0.9113 | 0.8526 | 0.7843 | 0.8226 | 0.8030 | 0.7976 | 0.8659 | 0.8304 | 0.9285 | 0.8384 | 0.8858 | 0.8607 | 0.9323 | 0.9285 | 0.9298 |
0.0052 | 35.0 | 2345 | 0.8586 | 0.9745 | 0.9412 | 0.9575 | 0.8021 | 0.9235 | 0.8586 | 0.7771 | 0.8318 | 0.8035 | 0.79 | 0.8827 | 0.8338 | 0.9298 | 0.8359 | 0.8948 | 0.8634 | 0.9346 | 0.9298 | 0.9313 |
0.0052 | 36.0 | 2412 | 0.9288 | 0.9698 | 0.9449 | 0.9572 | 0.8157 | 0.9067 | 0.8588 | 0.7864 | 0.8104 | 0.7982 | 0.7825 | 0.8641 | 0.8212 | 0.9286 | 0.8386 | 0.8815 | 0.8588 | 0.9320 | 0.9286 | 0.9298 |
0.0052 | 37.0 | 2479 | 0.9396 | 0.9684 | 0.9460 | 0.9570 | 0.8186 | 0.9037 | 0.8590 | 0.7824 | 0.8135 | 0.7976 | 0.7917 | 0.8566 | 0.8229 | 0.9288 | 0.8403 | 0.8799 | 0.8591 | 0.9317 | 0.9288 | 0.9298 |
0.0032 | 38.0 | 2546 | 0.9108 | 0.9706 | 0.9408 | 0.9555 | 0.8014 | 0.9067 | 0.8508 | 0.7743 | 0.8287 | 0.8006 | 0.7862 | 0.8696 | 0.8258 | 0.9268 | 0.8331 | 0.8865 | 0.8582 | 0.9310 | 0.9268 | 0.9282 |
0.0032 | 39.0 | 2613 | 0.8132 | 0.9757 | 0.9306 | 0.9526 | 0.7853 | 0.9174 | 0.8463 | 0.7249 | 0.8379 | 0.7773 | 0.7700 | 0.8976 | 0.8289 | 0.9224 | 0.8140 | 0.8959 | 0.8513 | 0.9299 | 0.9224 | 0.9247 |
0.0032 | 40.0 | 2680 | 0.9634 | 0.9692 | 0.9421 | 0.9554 | 0.8033 | 0.9052 | 0.8512 | 0.7876 | 0.8165 | 0.8018 | 0.7825 | 0.8641 | 0.8212 | 0.9266 | 0.8356 | 0.8820 | 0.8574 | 0.9304 | 0.9266 | 0.9279 |
0.0032 | 41.0 | 2747 | 0.9024 | 0.9711 | 0.9387 | 0.9546 | 0.7937 | 0.9174 | 0.8511 | 0.7655 | 0.8287 | 0.7959 | 0.7840 | 0.8585 | 0.8196 | 0.9253 | 0.8286 | 0.8858 | 0.8553 | 0.9301 | 0.9253 | 0.9269 |
0.0032 | 42.0 | 2814 | 0.9623 | 0.9682 | 0.9456 | 0.9567 | 0.8217 | 0.9021 | 0.8601 | 0.7922 | 0.8043 | 0.7982 | 0.7795 | 0.8622 | 0.8187 | 0.9283 | 0.8404 | 0.8786 | 0.8584 | 0.9314 | 0.9283 | 0.9294 |
0.0032 | 43.0 | 2881 | 0.9335 | 0.9692 | 0.9441 | 0.9565 | 0.8148 | 0.9083 | 0.8590 | 0.7811 | 0.8073 | 0.7940 | 0.7817 | 0.8603 | 0.8191 | 0.9278 | 0.8367 | 0.8800 | 0.8572 | 0.9312 | 0.9278 | 0.9290 |
0.0032 | 44.0 | 2948 | 0.8909 | 0.9714 | 0.9380 | 0.9544 | 0.7924 | 0.9220 | 0.8523 | 0.7642 | 0.8226 | 0.7923 | 0.7817 | 0.8603 | 0.8191 | 0.9250 | 0.8274 | 0.8857 | 0.8546 | 0.9300 | 0.9250 | 0.9266 |
0.0026 | 45.0 | 3015 | 0.9011 | 0.9711 | 0.9393 | 0.9549 | 0.7900 | 0.9205 | 0.8503 | 0.7876 | 0.8165 | 0.8018 | 0.7811 | 0.8641 | 0.8205 | 0.9259 | 0.8325 | 0.8851 | 0.8569 | 0.9306 | 0.9259 | 0.9274 |
0.0026 | 46.0 | 3082 | 0.9105 | 0.9709 | 0.9387 | 0.9546 | 0.7921 | 0.9205 | 0.8515 | 0.7801 | 0.8135 | 0.7964 | 0.7785 | 0.8641 | 0.8191 | 0.9253 | 0.8304 | 0.8842 | 0.8554 | 0.9301 | 0.9253 | 0.9268 |
0.0026 | 47.0 | 3149 | 0.9380 | 0.9698 | 0.9404 | 0.9549 | 0.7936 | 0.9113 | 0.8484 | 0.7811 | 0.8073 | 0.7940 | 0.7808 | 0.8622 | 0.8195 | 0.9253 | 0.8313 | 0.8803 | 0.8542 | 0.9296 | 0.9253 | 0.9267 |
0.0026 | 48.0 | 3216 | 0.9258 | 0.9702 | 0.9393 | 0.9545 | 0.7846 | 0.9190 | 0.8465 | 0.7843 | 0.8226 | 0.8030 | 0.7849 | 0.8492 | 0.8157 | 0.9249 | 0.8310 | 0.8825 | 0.8549 | 0.9295 | 0.9249 | 0.9264 |
0.0026 | 49.0 | 3283 | 0.9463 | 0.9697 | 0.9404 | 0.9548 | 0.7918 | 0.9128 | 0.8480 | 0.7836 | 0.8196 | 0.8012 | 0.7880 | 0.8585 | 0.8217 | 0.9257 | 0.8333 | 0.8828 | 0.8564 | 0.9300 | 0.9257 | 0.9271 |
0.0026 | 50.0 | 3350 | 0.9205 | 0.9708 | 0.9406 | 0.9555 | 0.7939 | 0.9190 | 0.8519 | 0.7895 | 0.8257 | 0.8072 | 0.7836 | 0.8566 | 0.8185 | 0.9266 | 0.8345 | 0.8855 | 0.8583 | 0.9310 | 0.9266 | 0.9280 |
0.0026 | 51.0 | 3417 | 0.9339 | 0.9702 | 0.9412 | 0.9555 | 0.8024 | 0.9128 | 0.8541 | 0.7872 | 0.8257 | 0.8060 | 0.7808 | 0.8622 | 0.8195 | 0.9269 | 0.8352 | 0.8855 | 0.8587 | 0.9310 | 0.9269 | 0.9283 |
0.0026 | 52.0 | 3484 | 0.9439 | 0.9712 | 0.9413 | 0.9560 | 0.7995 | 0.9205 | 0.8557 | 0.7959 | 0.8226 | 0.8090 | 0.7808 | 0.8622 | 0.8195 | 0.9276 | 0.8368 | 0.8867 | 0.8601 | 0.9319 | 0.9276 | 0.9290 |
0.0013 | 53.0 | 3551 | 0.9354 | 0.9715 | 0.9406 | 0.9558 | 0.7974 | 0.9266 | 0.8571 | 0.7855 | 0.8287 | 0.8065 | 0.7863 | 0.8566 | 0.8200 | 0.9275 | 0.8352 | 0.8881 | 0.8599 | 0.9319 | 0.9275 | 0.9289 |
0.0013 | 54.0 | 3618 | 0.9541 | 0.9715 | 0.9404 | 0.9557 | 0.7992 | 0.9251 | 0.8575 | 0.7832 | 0.8287 | 0.8053 | 0.7840 | 0.8585 | 0.8196 | 0.9273 | 0.8345 | 0.8882 | 0.8595 | 0.9318 | 0.9273 | 0.9288 |
0.0013 | 55.0 | 3685 | 0.9586 | 0.9715 | 0.9402 | 0.9556 | 0.7984 | 0.9266 | 0.8577 | 0.7820 | 0.8226 | 0.8018 | 0.7810 | 0.8566 | 0.8171 | 0.9269 | 0.8332 | 0.8865 | 0.8581 | 0.9314 | 0.9269 | 0.9284 |
0.0013 | 56.0 | 3752 | 0.9737 | 0.9690 | 0.9413 | 0.9549 | 0.8005 | 0.9083 | 0.8510 | 0.7853 | 0.8165 | 0.8006 | 0.7814 | 0.8585 | 0.8181 | 0.9259 | 0.8340 | 0.8811 | 0.8562 | 0.9298 | 0.9259 | 0.9272 |
0.0013 | 57.0 | 3819 | 0.9620 | 0.9695 | 0.9404 | 0.9547 | 0.7997 | 0.9098 | 0.8512 | 0.7807 | 0.8165 | 0.7982 | 0.7795 | 0.8622 | 0.8187 | 0.9256 | 0.8323 | 0.8822 | 0.8557 | 0.9298 | 0.9256 | 0.9270 |
0.0013 | 58.0 | 3886 | 0.9616 | 0.9697 | 0.9404 | 0.9548 | 0.7997 | 0.9159 | 0.8539 | 0.7853 | 0.8165 | 0.8006 | 0.7787 | 0.8585 | 0.8167 | 0.9259 | 0.8334 | 0.8828 | 0.8565 | 0.9301 | 0.9259 | 0.9273 |
0.0013 | 59.0 | 3953 | 0.9692 | 0.9701 | 0.9412 | 0.9554 | 0.8021 | 0.9174 | 0.8559 | 0.7830 | 0.8165 | 0.7994 | 0.7814 | 0.8585 | 0.8181 | 0.9266 | 0.8341 | 0.8834 | 0.8572 | 0.9307 | 0.9266 | 0.9280 |
0.001 | 60.0 | 4020 | 0.9704 | 0.9706 | 0.9413 | 0.9558 | 0.8027 | 0.9205 | 0.8575 | 0.7853 | 0.8165 | 0.8006 | 0.7817 | 0.8603 | 0.8191 | 0.9272 | 0.8351 | 0.8847 | 0.8583 | 0.9313 | 0.9272 | 0.9285 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for antoineedy/bert-base-NER-finetuned-ner
Base model
dslim/bert-base-NER